Various known navigation and positioning systems enable people in cars, boats, aircraft, and other moveable objects to efficiently travel between given locations. Knowing a precise current geographic location or starting location and a desired destination or ending location enables navigation systems to provide customized directions that indicate which direction that moveable object should travel to reach the destination or ending location. Various known navigation systems use path-planning algorithms that combine knowledge of conduits (such as streets, bridges, or traffic rules), obstacles (such as freeway congestion), and current real-time positioning information to determine and output detailed directions.
Various known navigation systems are enhanced through graphical user interfaces that visually depict the surroundings of a current position, identify points of interest, and provide a highlight of a path of travel to reach a destination. In one known example, vehicular navigation systems use the Global Positioning System (widely known as GPS). GPS is a space-based global navigation satellite system (GNSS) that provides reliable location and time information to anyone on or near the Earth.
One known limitation of existing navigation systems that employ GPS is that they typically need an unobstructed line of sight to multiple (such as four or more) GPS satellites to receive and calculate a geographic position of an object. For this reason, GPS typically does not effectively operate in indoor areas or spaces such as in buildings or other covered structures. Thus, while GPS has become a valued system for outdoor navigation, GPS is generally unsuited for indoor navigation.
Various existing indoor navigation systems use radio or sound waves to determine a current position of a moveable object in an indoor area. One known indoor navigation system determines a location using Received Signal Strength Indicator (“RSSI”) values of multiple Wi-Fi beacons (i.e., IEEE 802.11 access points or radios). This system is configured to use location fingerprinting, which stores samples of RSSI values of received Wi-Fi signals transmitted by a number of locations in a mapped area. In this location fingerprinting system, a processor computes a current location of a moveable object by sampling the RSSI values and performing a look-up within a database.
Another known indoor navigation system determines a location of a moveable object using triangulation of RSSI values of multiple Wi-Fi beacons. This system uses triangulation to compute expected signal strengths at a given location using signal propagation equations that estimate effects of known obstructions and multipath errors.
One known problem of using location fingerprinting or triangulation in indoor areas is that both of these methods are limited in accuracy to within a few meters, and tend to worsen with dynamic changes in signal obstructions resulting from human movement or physical obstructions including, for example, walls, shelves, signs, etc. Similar methods using Bluetooth or Near Field Communication (“NFC”) signals also experience the same challenges in indoor areas.
Since all of these indoor navigation systems have various known issues or problems, the overall need for indoor navigation systems remains an issue largely unaddressed by currently known commercially available navigation systems. Accordingly, a need exists for better indoor navigation systems.